CN116574813A

CN116574813A - Application of exosome miRNA biomarker in diagnosis and early warning of fatty liver in dairy cow perinatal period

Info

Publication number: CN116574813A
Application number: CN202310585653.XA
Authority: CN
Inventors: 师科荣; 侯宪朋; 刘廷俊; 闫晓冉; 范思思; 张璇
Original assignee: Shandong Agricultural University
Current assignee: Shandong Agricultural University
Priority date: 2023-05-23
Filing date: 2023-05-23
Publication date: 2023-08-11

Abstract

The application discloses application of an exosome miRNA biomarker in diagnosis and early warning of fatty liver in a dairy cow perinatal period, and belongs to the technical field of biology. The 12 miRNA biomarkers with diagnostic value are found in the corresponding disease stage of the fatty liver disease of the dairy cows, and at the identification, early warning, identification and diagnosis, particularly at least 1 of 3 miRNAs can be extracted and enriched without exosome, so that whether the dairy cows are ill or not can be diagnosed in serum miRNAs. The biomarker provided by the application is used for identifying and diagnosing the fatty liver dairy cows, has higher specificity and sensitivity, stable property of the marker, simple and convenient operation and lower price, is a noninvasive and noninvasive detection means, is harmless and harmless to the cows, accords with the ideas of animal welfare and healthy cultivation, can be widely applied to large-scale cultivation of the dairy cows in the future, and promotes healthy and efficient development of the milk industry.

Description

Application of exosome miRNA biomarker in diagnosis and early warning of fatty liver in dairy cow perinatal period

Technical Field

The application relates to the technical field of biology, in particular to application of an exosome miRNA biomarker in diagnosis and early warning of fatty liver in the perinatal period of cows.

Background

As a metabolic disorder disease of dairy cows Gao Fayi in the perinatal period (the period from three weeks before the birth to three weeks after the delivery of the dairy cows is called as the perinatal period), researches show that 5-10% of the dairy cows suffer from severe fatty liver and 30-40% suffer from moderate or mild fatty liver in the perinatal period, and the occurrence of the disease can reduce the milk yield and the production reproductive performance of the dairy cows, so that a higher cow elimination rate is caused. Therefore, the accurate diagnosis of fatty liver is beneficial to the health of cows and the sustainability of cultivation; the method can also be adjusted and prevented in advance through early warning diagnosis, so that economic loss of pasture caused by milk yield reduction is reduced; meanwhile, the elimination rate of the dairy cows in the perinatal period is reduced, and the economic loss caused by elimination of the cows is avoided.

At present, the detection method of fatty liver mainly comprises liver biopsy, serum physiology and biochemistry, proteomics and metabonomics, digital image technology and the like. Among them, the most accurate and direct method is liver biopsy, but it requires operation to take liver, causes secondary injury to cow body, and increases loss. The currently accepted diagnostic method is the Y-value method proposed by Reid (Reid et sl., 1983), which requires 3 serum biochemical indexes to calculate, but cannot accurately judge the onset degree of fatty liver, has poor accuracy, and is not suitable for large-scale pastures (Hu et al, 2018; zhang et al, 2022). Thus, there is an urgent need in the industry to develop and research new methods for diagnosis that are efficient, accurate, convenient to operate, and substantially harmless and even non-invasive.

MiRNA (microRNA) is a small non-coding RNA of about 22nt in length with regulatory functions that are produced by almost all cells in the body (Bartel, 2018; ha et al, 2014). Studies have shown that mirnas are involved in almost all developmental and pathological processes in animals (Ha and Kim, 2014). And researchers have found that certain circulating mirnas can diagnose, judge and prognosis many types of diseases, functioning as biomarkers (Jamali et al, 2018).

Exosomes are extracellular vesicles with a diameter ranging from 40 to 160nm having endosomal origin (Kalluri et al 2020). Exosomes are widely distributed in various body fluids such as blood, saliva, cerebrospinal fluid, breast milk, and urine, which can carry biomolecules including mirnas (Xu et al, 2022). With further advances in research, exosomes are becoming a potentially valuable non-invasive liquid biopsy tool for the fatty liver progression of perinatal cows.

Mirnas are more desirable biomarker candidates than other nucleic acids. The long-lasting stability of mirnas in blood, and mirnas can still exist relatively stably without degradation in the presence of rnases (Mitchell et al, 2008), makes their use as non-invasive biomarkers of great value in diagnosis (Wang et al, 2009). The use of mirnas as biomarkers has the advantages of rapidness, sensitivity, non-invasiveness, economy, high efficiency, and the like, which also makes mirnas an effective biomarker for diagnosis and prognosis of various diseases (Jamali et al, 2018). The inventor team of the patent detects partial miRNAs in serum of suspected fatty liver cows by means of the differential miRNAs found by human, mouse and rat fatty liver models in early-stage researches (CN 105420405B), finally obtains a group of bovine serum miRNA molecular markers which can be used for predicting fatty liver of cows and related metabolic diseases of cows in perinatal period, and can be used for early discovery and diagnosis of diseased cows in cow production practice by using the molecular markers. However, the following problems still remain in the foregoing studies:

(1) The miRNA molecular markers are not directly derived from cattle, and the specificity and sensitivity of the miRNA molecular markers are required to be improved.

(2) The probability of the dairy cow suffering from the fatty liver disease is judged based on the change of several biochemical indexes, and accurate diagnosis is not considered, but only suspected disease is considered, so that the diagnosis method has certain limitation and large error (one study indicates that the error of the fatty liver prevalence rate of the dairy cow in the perinatal period can be about 30-50 percent, mainly caused by the time sequence and physiological variation of the biochemical indexes in serum, and the variation can cause difficult judgment and misdiagnosis). Correspondingly, the sensitivity and specificity of the bovine serum miRNA molecular markers for diagnosis are still to be further improved.

Disclosure of Invention

Aiming at the prior art, the application aims to provide application of an exosome miRNA biomarker in diagnosis and early warning of the fatty liver in the perinatal period of cows.

In order to achieve the above purpose, the application adopts the following technical scheme:

in a first aspect of the application, there is provided the use of at least one exosome miRNA as defined in 1) -12) as a biomarker in the preparation of a kit for diagnosing or pre-warning a perinatal dairy cow of fatty liver disease:

1) The nucleotide sequence of the bta-miR-2285bn is shown as SEQ ID NO. 1;

2) The nucleotide sequence of bta-miR-2285ce is shown as SEQ ID NO. 2;

3) The nucleotide sequence of bta-miR-10225b is shown as SEQ ID NO. 3;

4) The nucleotide sequence of bta-miR-369-5p is shown as SEQ ID NO. 4;

5) The nucleotide sequence of the bta-miR-219-3p is shown as SEQ ID NO. 5;

6) The nucleotide sequence of the bta-miR-11988 is shown as SEQ ID NO. 6;

7) The nucleotide sequence of the bta-miR-296-3p is shown as SEQ ID NO. 7;

8) The nucleotide sequence of the bta-miR-10174-3p is shown as SEQ ID NO. 8;

9) The nucleotide sequence of the bta-miR-378c is shown in SEQ ID NO. 9;

10 bta-miR-27a-3p, the nucleotide sequence of which is shown as SEQ ID NO. 10;

11 A nucleotide sequence of novel_118 is shown as SEQ ID NO. 11;

12 No. 173, the nucleotide sequence of which is shown in SEQ ID NO. 12.

The 12 exosome miRNAs can be used as biomarkers to accurately identify the fatty liver of the dairy cows in the perinatal period, and the AUC value of each exosome miRNA accords with diagnostic significance, so that the method has higher clinical diagnosis application value; moreover, when a plurality of exosome miRNAs are used in combination, the AUC is closer to 1 than that of a single one, and the diagnosis effect is better.

Further, when a plurality of exosome miRNAs are jointly applied, different diagnosis effects can be realized based on different exosome miRNA combinations, and the method is specific:

the 3 exosome miRNAs of novel_118, bta-miR-2285bn and bta-miR-2285ce are combined for use, and can be used for diagnosing moderate fatty liver of the perinatal cows.

The 7 exosome miRNAs of bta-miR-369-5p, bta-miR-219-3p, bta-miR-10225b, novel_173, bta-miR-11988, bta-miR-10174-3p and bta-miR-378c are combined for use, and can be used for diagnosing severe fatty liver of the dairy cows in perinatal period.

The 3 exosome miRNAs of novel_173, bta-miR-378c and bta-miR-27a-3p are used in combination, so that the moderate fatty liver and the severe fatty liver of the perinatal dairy cows can be distinguished.

The 6 exosome miRNAs of bta-miR-369-5p, novel_118, bta-miR-10225b, bta-miR-219-3p, bta-miR-296-3p and bta-miR-10174-3p are combined for use, and can be used for distinguishing perinatal fatty liver cows from normal cows.

In a second aspect of the application, there is provided the use of an agent for detecting exosome miRNA in the manufacture of a product for non-invasively identifying fatty liver disease in a perinatal cow;

the exosome miRNA is the miRNA shown in at least one of SEQ ID NO.1-SEQ ID NO. 12.

Further, the reagent is a reagent for detecting miRNA in the serum exosomes of the cows, or a reagent for directly detecting miRNA in the serum.

Preferably, the reagent contains a qPCR detection primer, and the sequence of the qPCR detection primer is shown as SEQ ID NO.16-SEQ ID NO. 27.

The application has the beneficial effects that:

(1) The 12 miRNA biomarkers with diagnostic value are found in the corresponding disease stage of the fatty liver disease of the dairy cows, and at the identification, early warning, identification and diagnosis, particularly at least 1 of 3 miRNAs can be extracted and enriched without exosome, so that whether the dairy cows are ill or not can be diagnosed in serum miRNAs. The biomarker provided by the application is used for identifying and diagnosing the fatty liver dairy cows, has higher specificity and sensitivity, stable property of the marker, simple and convenient operation and lower price, is a noninvasive and noninvasive detection means, is harmless and harmless to the cows, accords with the ideas of animal welfare and healthy cultivation, can be widely applied to large-scale cultivation of the dairy cows in the future, and promotes healthy and efficient development of the milk industry.

(2) The miRNA biomarker provided by the application is combined with screening and verification of 43 perinatal cows, has higher sensitivity and specificity in fatty liver identification and diagnosis, and has great use value and significance in future perinatal cow fatty liver diagnosis.

(3) The miRNA biomarker screened by the application is derived from cattle and has stronger pertinence; the health condition of the dairy cow is determined after accurate diagnosis by a liver biopsy method, and the health or disease degree of the dairy cow can be diagnosed by 100%; based on the improvement, miRNA markers discovered by the application are more sensitive and have stronger specificity than miRNAs markers (bovine serum microRNA molecular markers of dairy cow fatty liver diseases and dairy cow perinatal related metabolic diseases) discovered by the team in the past.

Drawings

Fig. 1: the discovery flow chart of the biomarkers of the application.

Fig. 2: the fat content of liver tissues of each group of dairy cow groups was sequenced.

Fig. 3: volcanic patterns of differential miRNAs between groups. Screening criteria |log ₂ (FC)|≥0.5；P≤0.05。

Fig. 4: venn plot of differential miRNAs between groups. Volcanic image screening results yielded a total of 28 (with duplicate mirnas removed) differentially expressed mirnas.

Fig. 5: results of 12 (with duplicate mirnas removed) miRNA wien plots between each group obtained by ROC curve screening analysis. The screening criteria were AUC > 0.7 (while P < 0.05). In addition, SFL vs MFL group screening criteria were AUC > 0.7.

Fig. 6: and screening ROC curves of 3 miRNAs by using the MFL vs Norm group ROC curves, and jointly diagnosing the ROC curves by using the 3 miRNAs. The screening standard is AUC > 0.7, P < 0.05.

Fig. 7: SFL vs Norm ROC curve screening results in ROC curves of 7 miRNAs and joint diagnosis of the ROC curves of 7 miRNAs. The screening standard is AUC > 0.7, P < 0.05.

Fig. 8: SFL vs MFL group ROC curve screening results in ROC curves of 3 miRNAs and the group 3 miRNAs are combined to diagnose the ROC curves. The screening criteria was AUC > 0.7.

Fig. 9: and screening the FL vs Norm ROC curves to obtain ROC curves of 6 miRNAs and jointly diagnosing the ROC curves of the 6 miRNAs. The screening standard is AUC > 0.7, P < 0.05.

Fig. 10: and screening the obtained 12 miRNA sequencing expression quantity violin images.

Fig. 11: and verifying the fat content of liver tissues of each group of cows in the new group. Verifying three groups of 12 cows with health conditions determined by liver biopsy diagnosis of the new group,

fig. 12: 3 miRNA validation cases that are still diagnostic in serum miRNAs. The left ordinate of the upper three pictures in the figure and the red broken line, the round black point and the histogram represent the expression condition of the miRNA in the serum verification set; the right ordinate and blue broken line in the figure indicate the expression of the miRNA in the sequencing set.

Detailed Description

It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

As described above, due to the error of the source of miRNA and the method used to determine whether the cow has fatty liver, the specificity and accuracy of diagnosing the fatty liver of the cow with the bovine serum miRNA developed by the inventor team in the earlier study still needs to be further improved.

In view of this, the present application has further studied this. Serum exosomes contain a high number of miRNAs. By enriching exosomes, more miRNAs can be enriched into the database for subsequent screening. Therefore, the application starts from serum exosome miRNA, and the exosome miRNA which can diagnose or early warn the fatty liver disease of the perinatal dairy cows is screened.

The discovery process of the exosome RNA biomarker is shown in figure 1, and is specifically as follows:

the perinatal cow population that had been identified by liver biopsy was divided into a sequencing set (n=31) and a serum validation set (n=12). In the sequencing set (n=31), norm (normal group, n=12), MFL (moderate fatty liver group, n=8), SFL (severe fatty liver group, n=11). Screening serum exosome miRNA markers in the serum of the sequencing dairy cows, and firstly carrying out Small RNA-seq on the serum to obtain all miRNA expression profiles.

Furthermore, using a multivariate statistical orthogonal partial least squares discriminant analysis (OPLS-DA) model, the dimension reduction process (supervised classification model) can be performed based on a nonlinear iterative partial least squares algorithm. The method can use orthogonal partial least squares regression to establish a relation model between miRNA expression quantity which is required to be obtained by sequencing and sample types, and can reflect differences among classification groups to the greatest extent so as to realize modeling prediction of the sample types. And VIP values for each miRNA were calculated as subsequent references.

Further, the miRNAs obtained by sequencing are screened for different miRNAs between groups according to different groups. Statistical analysis was performed on all miRNAs expressed in the pairwise comparison groups MFL vs Norm, SFL vs Norm and SFL vs MFL, with DESeq2 based on negative binomial distribution (Love et al, 2014). The screening criteria for differential genes were |log ₂ (FC) | is more than or equal to 0.5; p is less than or equal to 0.05, and results among groups are shown by a differential volcanic chart.

Further, the diagnostic value of the resulting miRNAs was assessed by ROC curve screening analysis for the obtained miRNAs differentially expressed between the groups. When the AUC of the ROC curve is between 0.7 and 0.9 (with P < 0.05), this indicates a moderate accuracy. When the ROC curve AUC > 0.9 (while P < 0.05), it is shown to have high accuracy. miRNAs with AUC more than 0.7 (meanwhile P less than 0.05) are selected from each group, miRNAs with more than moderate diagnostic ability are obtained through screening, and the obtained miRNAs in each group are used as candidate miRNA biomarkers.

Further, MFL group and SFL group were regarded as diseased group, denoted as FL group, and the diagnostic value of the obtained mirnas was evaluated by ROC curve screening analysis as well. The screening criteria were likewise AUC > 0.9, P < 0.05.

At this time, each obtained miRNAs in each group is a perinatal dairy cow serum exosome miRNA with medium or higher diagnostic capability on fatty liver, and the combined diagnostic result of the miRNAs in the groups shows that the combination has higher diagnostic application value. Since SFL vs MFL gave fewer markers, the screening range was extended, with a screening criterion of AUC > 0.7.

Further, the candidate miRNAs obtained by screening and analyzing ROC curves in each group are subjected to joint diagnosis analysis, and the Logistic regression analysis and the ROC curve analysis are used for joint diagnosis analysis, so that AUC (AUC > 0.7) values, P values (P < 0.05) and joint diagnosis ROC curves of the joint diagnosis analysis are obtained.

Furthermore, because exosome extraction conditions are severe, the method is particularly characterized by complex operation and long time consumption, the method does not enrich exosomes when the serum verification set is used for verification, and directly extracts miRNAs in the serum of the dairy cows in the perinatal period of the health condition which is diagnosed by liver biopsy.

So in the serum validation set (n=12) (cows have been biopsied for diagnosis of health status), norms (normal, n=4), MFLs (moderate fatty liver, n=4), SFLs (severe fatty liver, n=4). The serum verification set is not subjected to exosome extraction and enrichment, and the sequencing-screened candidate exosome miRNA markers are directly subjected to RT-qPCR verification and ROC curve diagnosis analysis verification in serum miRNAs.

The verification method adopts a mode of combining internal reference and external reference, wherein the internal reference adopts U6, and the external reference selects a cel-miR-39-3p external reference standard which does not exist in the dairy cows. By comparison, and considering that adding external references increases the verification burden when specifically used, the present application can directly use internal references U6 for subsequent quantification when specifically used.

And carrying out RT-qPCR verification and ROC curve diagnosis analysis verification on the obtained miRNA by using a tailing method, and analyzing and verifying whether the candidate serum exosome miRNAs have diagnostic capability and value if the serum miRNAs are not enriched by exosomes.

The application finally discovers 12 serum exosome miRNA biomarkers with diagnostic value, and the 12 serum exosome miRNA biomarkers are respectively:

at least one of the 12 serum exosome miRNA biomarkers can be used as a non-invasive serum exosome miRNA biomarker for identifying a fat liver cow with corresponding disease degree, wherein the bta-miR-369-5p, bta-miR-219-3p, bta-miR-10225b, bta-miR-10174-3p, bta-miR-2285bn, bta-miR-2285ce, bta-miR-11988, bta-miR-378c, bta-miR-296-3p, bta-miR-27a-3p, novel_118 and novel_173; and 3 miRNAs are found, wherein at least 1 of the miRNAs are bta-miR-369-5p, bta-miR-219-3p and bta-miR-10174-3p, and the method can directly perform fatty liver recognition, early warning or diagnosis of the perinatal dairy cows in serum miRNAs without exosome extraction and enrichment.

The sequence information of the 12 serum exosome miRNAs with diagnostic value is shown in SEQ ID NO.1-SEQ ID NO.12 in table 1 and a sequence table:

table 1: miRNA mature sequence and miRBase database accession number

Note that: uracil "U" in the miRNA sequences in the tables is denoted by "T" in the sequence Listing, as specified by WIPO ST.26 standard.

In order to enable those skilled in the art to more clearly understand the technical scheme of the present application, the technical scheme of the present application will be described in detail with reference to specific embodiments.

The test materials used in the examples of the present application, which are not specifically described, are all conventional in the art and are commercially available.

Example 1: the application relates to biological sample conditions and methods

Biological samples were divided into sequencing sets (n=31) and serum validation sets (n=12) using perinatal chinese holstein cows as biological samples.

The degree of the cows suffering from fatty liver was classified according to the fat content results of the collected liver tissue (for diagnosis see fig. 2), and all sequencing samples were divided into three groups (n=31) according to the fat content of the affected cows: norms (normal) fat content was 0.082% ± 0.071% (n=12); fat content of MFL group (moderate fatty liver group) was 29.49% ± 6.30% (n=8); SFL group (severe fatty liver group) fat content was 74.70% ± 8.23% (n=11). And extracting exosomes, extracting total exosome RNA and sequencing small RNA.

Example 2: preliminary screening by differential expression analysis

Small RNA sequencing in example 1 yielded 309 miRNAs in total. According to |log by DESeq2 analysis ₂ (FC) | is more than or equal to 0.5, and P is less than or equal to 0.05 (shown in figure 3), three groups are obtained, and MFL vs Norm comparison groups (7) are obtained, wherein the obtained miRNAs are bta-miR-22-3p, bta-miR-3600, novel_118, bta-miR-2285bn, bta-miR-2285ce, bta-miR-219-3p and bta-miR-424-5p; SFL vs Norm comparison group (20), the obtained miRNAs are bta-miR-219-3p, bta-miR-424-5p, bta-miR-296-3p, bta-miR-10174-3p, bta-miR-124a, bta-miR-11988, bta-miR-2376, bta-miR-369-5p, bta-miR-10225a, bta-miR-1388-5p, bta-miR-10225b, bta-miR-411c-5p, bta-miR-30f, novel_72, bta-miR-221, bta-miR-6529a, bta-miR-6529b, bta-miR-409b, novel_173, bta-miR-378c; SFL vs MFL comparison group (8), the obtained miRNAs are bta-miR-6529a, bta-miR-6529b, bta-miR-409b, novel_173, bta-miR-378c, bta-miR-27a-3p, bta-miR-425-5p and bta-miR-1468, and repeated miRNAs are removed, and 28 miRNAs with obvious differential expression are taken out (see figure 4).

Example 3: obtaining miRNA biomarker through ROC curve screening

Further diagnostic capacity ROC screening analysis was performed on 28 significantly differentially expressed miRNAs initially screened in example 2, and miRNAs with AUC > 0.7 and P < 0.05 were selected to obtain MFL vs Norm (3) (see Table 2), SFL vs Norm (7) (see Table 3), SFL vs MFL (3) (see Table 4), FL vs Norm (6) (see Table 5), and duplicate miRNAs were removed for a total of 12 diagnostic-purpose perinatal cow serum exosome miRNA biomarkers (see FIG. 5): bta-miR-2285bn, bta-miR-2285ce, novel_118, bta-miR-11988, bta-miR-369-5p, bta-miR-219-3p, bta-miR-10225b, bta-miR-10174-3p, novel_173, bta-miR-378c, bta-miR-296-3p and bta-miR-27a-3p. The sequencing expression level violin diagram of the 12 miRNAs is shown in FIG. 10.

Table 2: MFL vs Norm diagnostic moderate ROC analysis results (3 miRNAs+ Combined diagnostic analysis)

Table 3: SFL vs Norm diagnosis of severe ROC analysis results (7 miRNAs)

Table 4: SFL vs MFL diagnosis of severe ROC analysis results (3 miRNAs) from individuals with disease

Table 5: FL vs Norm diagnosis of diseased ROC analysis results (6 miRNAs)

Example 4: the miRNA biomarkers obtained from each group are respectively subjected to joint diagnosis analysis

In order to improve the diagnostic efficacy of the miRNA biomarkers obtained in example 3, the mirnas obtained in each group were subjected to joint diagnostic analysis, as shown in fig. 6 and table 2, and MFL vs Norm compares the auc=1 (p=0.0001) of the joint diagnosis of the 3 mirnas, which has a higher diagnostic effect; as shown in fig. 7 and table 3, the SFL vs Norm comparison group 7 mirnas combined diagnosis auc=1 (P < 0.0001) has higher diagnosis effect; as shown in fig. 8 and table 4, the SFL vs MFL comparison group of 3 mirnas combined diagnosis auc=0.921 (p=0.002) also has higher diagnosis effect; as shown in fig. 9 and table 5, FL vs Norm comparison group 6 mirnas combined diagnosis auc=1 (P < 0.0001) has higher diagnosis effect.

Therefore, based on the combination of different exosome miRNA biomarkers, different diagnostic effects can be achieved, in particular:

the combined use of the 3 exosome miRNAs of novel_118, bta-miR-2285bn and bta-miR-2285ce (see Table 2) can be used for diagnosing the moderate fatty liver of the perinatal dairy cows.

The 7 exosome miRNAs of bta-miR-369-5p, bta-miR-219-3p, bta-miR-10225b, novel_173, bta-miR-11988, bta-miR-10174-3p and bta-miR-378c are used in combination (see table 3), and can be used for diagnosing severe fatty liver of the dairy cows in perinatal period.

The 3 exosome miRNAs of novel_173, bta-miR-378c and bta-miR-27a-3p (see Table 4) are used in combination and can be used for distinguishing the moderate fatty liver from the severe fatty liver of the perinatal dairy cows.

Considering that in the actual production process, the diagnosis of the fatty liver should mainly be solved, firstly, whether the raised cows have fatty liver (regardless of the degree of the disease) is distinguished, and secondly, the degree of the disease of the dairy cows is distinguished so as to treat the cows according to the symptoms, so that resources such as medical treatment and manpower are saved, and the like are saved, therefore, 6 exosome miRNAs of bta-miR-369-5p, novel_118, bta-miR-10225b, bta-miR-219-3p, bta-miR-296-3p and bta-miR-10174-3p are used in combination (see table 5), and can be used for distinguishing the cows with the fatty liver in the perinatal period from the normal cows.

The method for diagnosing the disease condition of the dairy cows in the perinatal period by using the exosome miRNA biomarker comprises the following steps:

collecting blood samples of normal cattle and suspected cattle or cattle to be diagnosed, separating serum, extracting serum miRNAs (the miRNAs can be enriched without exosomes), detecting the relative expression quantity of the marker miRNAs by using a real-time fluorescent quantitative PCR technology, and comparing the relative expression quantity with the expression level of the normal cattle, wherein the relative expression quantity has the same change trend with the expression level of the normal cattle in fatty liver, so that the suspected cattle have hidden danger.

Criteria for judgment: log2 (FC) >0 is elevated; log2 (FC) <0 is a decrease.

When bta-miR-2285bn or bta-miR-2285ce in Table 2 is used as a diagnostic marker, if the bovine to be tested is expressed in a higher level than normal Niu Chengshang liter, the bovine to be tested is indicated to have the possibility of suffering from moderate fatty liver. Conversely, if bta-miR-10174-3p (shown in Table 3) is used as a diagnostic marker, the expression level of the bta-miR-10174-3p in the bovine serum to be tested is reduced compared with that of a normal bovine, so that the bovine to be tested is likely to have severe fatty liver.

Example 5: directly verifying miRNA biomarker in serum miRNA without exosome extraction and enrichment

In the serum validation set (n=12) (as in fig. 11): the fat content of the Norm group (normal group, n=4) was 0.28% ± 0.34%; the fat content of MFL group (moderate fatty liver group, n=4) was 32.75% ± 6.21%; the fat content of SFL group (severe fatty liver, n=4) was 73.09% ± 8.03%. For the convenience of application, the operation steps are reduced, the exosome extraction and enrichment are not carried out on the new population, and RT-qPCR verification and ROC curve diagnosis verification are directly carried out on the exosome miRNA markers screened by the sequencing set in serum.

Further, the mode of combining internal reference and external reference is used in verification, U6 (the primer sequences are shown in Table 6 and SEQ ID NO.13-14 in the sequence table) is used as the internal reference, and the external reference is selected from cel-miR-39-3p external reference standard substances which are not existing in dairy cows (the primer sequences are shown in Table 6 and SEQ ID NO.15 in the sequence table). By comparison and considering that the addition of external reference increases the verification burden when the miRNA biomarker is specifically used, the application can directly use the internal reference U6 for subsequent quantification when the miRNA biomarker is specifically used, adopts a poly-A tailing reverse transcription method to verify miRNA to be detected, uses a miRcute enhanced miRNA fluorescence quantitative detection kit (SYBR Green) (FP 411) for detection, and the quantitative primer sequences used for detection of each miRNA biomarker are shown in Table 7 and SEQ ID NO.16-27 in the sequence table.

The sequence (5 '-3') of the cel-miR-39-3p (MIMAT 0000010) is as follows: UCACCGGGUGUAAAUCAG CUUG. (SEQ ID NO. 28)

Note that: uracil "U" in the miRNA sequence is denoted by "T" in the sequence Listing, as specified by WIPO ST.26 standard.

Table 6: internal and external references and primers used in verification process

Table 7: miRNA forward primer sequence

Candidate miRNAs with AUC > 0.7 and P < 0.05 were selected, and SFL vs MFL groups (3) were obtained (see Table 8, FIG. 12): bta-miR-369-5p (auc=1, p=0.0209), bta-miR-219-3p (auc=0.9375, p=0.0433), and bta-miR-10174-3p (auc=1, p= 0.0495). FL vs Norm group (2) (see table 9, fig. 12): bta-miR-369-5p (auc=1, p=0.0082), bta-miR-10174-3p (auc=1, p= 0.0201). The methods for extracting miRNA by the front method and the rear method are different, and the detection methods are different, but the change trend between the two methods is still consistent. Again, these mirnas were demonstrated for reliability of diagnostic markers.

Table 8: SFL vs MFL group-verification of diagnostic ability of candidate miRNAs in serum miRNAs and trend of variation thereof

Note that: the results shown in the table "change case (sequencing set)" are the results of the change of the miRNA in the SFL vs MFL group obtained from the sequencing data, and are shown here for visual comparison with the RT-qPCR validation data of the miRNA in the SFL vs MFL group. The methods for extracting miRNA by the front method and the rear method are different, and the detection methods are different, but the change trend between the two methods is still consistent. Again, these mirnas were demonstrated for reliability of diagnostic markers.

Table 9: FL vs Norm group-validation of diagnostic ability of candidate miRNAs in serum miRNAs

Therefore, 3 miRNAs, namely bta-miR-369-5p, bta-miR-219-3p and bta-miR-10174-3p, still have diagnostic significance in serum directly.

In laboratory studies, more miRNAs can be obtained by exosome enrichment for differential expression analysis or research analysis, thus screening biomarkers from exosome miRNAs; in addition, considering the actual production and application process, most of the breeding environments and detection mechanisms at present do not have the capability of extracting exosomes, so the detection capability of the exosome miRNA biomarker in serum is verified, and the result shows that: miRNAs (bta-miR-369-5 p, bta-miR-219-3p and bta-miR-10174-3 p) used for diagnostic markers can be directly detected and diagnosed without exosome enrichment, and are still feasible.

Example 6: in combination with previous laboratory study, the screening result is compared and analyzed

Comparison with previous studies in the laboratory (CN 105420405B) shows that during this sequencing, the remaining miRNAs were not significantly changed or biased or even undetectable during the screening process of the present application, except for the two miRNAs of the same family obtained according to the present application (miR-27 a-3p in the miRBase database under accession No. MI0000860; bta-miR-27a-3p in the present application under accession No. MIMAT 0003532) and miR-378a-3p in the miRBase database under accession No. MI0003719; bta-miR-378c in the present application under accession No. MIMAT 0025551).

Table 10: the miRNA obtained by the application corresponds to the miRNA obtained by the original application ¹

Note that: 1. the 6 miRNAs listed in this table are the serum expression information obtained from the sequencing raw data from which the application was derived, the remaining miRNAs.

2. Accession numbers for each miRNA are from the miRBase database.

Na indicates no detection value in the sample sequencing data of the present application.

In contrast, the marker identified by the application is more suitable for being used as a diagnosis marker of the dairy cow perinatal fatty liver and metabolic disorder, and has higher sensitivity and specificity. The reasons are three: firstly, the source groups screened by the application are the diseased cattle group and the normal cattle group diagnosed by the gold standard liver biopsy, so that the miRNAs obtained by screening have stronger pertinence and higher accuracy. Secondly, the sensitivity and the accuracy of the miRNAs serving as diagnostic markers are verified by ROC analysis; again, the miRNAs of the application are still diagnostic in accuracy as verified by his cohort.

The original patent reports miRNAs according to human, mouse and rat literature, then selects a plurality of miRNAs to detect in bovine serum, and finally the patent shows that a plurality of miRNAs are marked by the marked difference expression in bovine serum. The one-sided performance of the milk cow accurate diagnosis is visible. This is also the most causative factor of the present application in targeting screening markers based on fatty liver cows/normal cows diagnosed by "gold standard" -liver biopsy. In addition, the cow group in the previous patent judges the possibility of suffering from fatty liver disease of the cow based on the change of several biochemical indexes, and the cow group is not accurately diagnosed but suspected to suffer from the fatty liver disease.

In conclusion, the exosome miRNA biomarker identified by the application is more suitable for being used as a diagnosis marker of the dairy cow perinatal fatty liver and metabolic disorder, and has higher sensitivity and specificity.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

Application of at least one exosome miRNA of SEQ ID NO.1-SEQ ID NO.12 as a biomarker in preparing a kit for diagnosing or early warning fatty liver diseases of perinatal cows.
2. Application of exosome miRNA combination as biomarker in preparing a product for diagnosing moderate fatty liver of perinatal cows;

the exosome miRNA combination consists of exosome miRNAs shown in SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO. 11.
3. Application of exosome miRNA combination as biomarker in preparing a product for diagnosing severe fatty liver of perinatal cows;

the exosome miRNA combination consists of exosome miRNAs shown in SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.8, SEQ ID NO.9 and SEQ ID NO. 12.
4. Application of exosome miRNA combination as biomarker in preparation of products for distinguishing moderate fatty liver and severe fatty liver of perinatal cows;

the exosome miRNA combination consists of exosome miRNAs shown in SEQ ID NO.9, SEQ ID NO.10 and SEQ ID NO. 12.
5. Application of exosome miRNA combination as biomarker in preparation of products for distinguishing perinatal fatty liver cows and normal cows;

the exosome miRNA combination consists of exosome miRNAs shown in SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5, SEQ ID NO.7, SEQ ID NO.8 and SEQ ID NO. 11.
6. Use of a reagent for detecting exosome miRNA in the preparation of a product for non-invasively identifying perinatal dairy cow fatty liver disease;

the exosome miRNA is the miRNA shown in at least one of SEQ ID NO.1-SEQ ID NO. 12.
7. The use according to claim 6, wherein the reagent is a reagent for detecting miRNA in the serum exosomes of cows, or a reagent for directly detecting miRNA in serum.
8. The use according to claim 6 or 7, wherein the reagent comprises a qPCR detection primer having the sequence shown in SEQ ID No.16-SEQ ID No. 27.